1. Field of the Invention
The present invention relates to a data processing apparatus which integrates a neuro-computer for flexible data processing or a connection machine for multifunction data processing.
2. Description of the Prior Art
FIG. 1 illustrates the construction of a prior art data processing apparatus such as one described in Photo- and Quantum-Electronics Study Group Material OQE87-174 (1988), pp. 39-45, Japan Society of Electronic Information Telecommunications. In FIG. 1, reference character 1 is a data input section that admits incomplete data; 2a (or 2b) is an LED (light-emitting diode) array whose elements correspond to the component pieces of the incomplete data; 3a (or 3b) is an optical mask having at least one piece of accumulated data in connection with each component of the incomplete data, the accumulated data being a collection of transmission factors; 4a (or 4b) is a light-receiving photodiode (PD) array whose elements correspond to the component pieces of the result of matrix computation with the optical mask 3a (or 3b); 5 is a differential amplifier that addresses the difference between the PD arrays 4a and 4b; 6 is a comparator that compares the output from the differential amplifier 5 with a threshold value; and 7 is a data output section that outputs complete data (i.e., corrected result of matrix computation).
In operation, the data input section 1 admits incomplete data and outputs it to the LED arrays 2a and 2b and to the data output section 7. After the output, the data input section 1 remains inactive until the next input of incomplete data. Each element of LED array 2a (or 2b) is turned ON or OFF according to the respective component pieces of the input data, EMITTING or NOT EMITTING a fan-shaped beam of light, as a component Vj of an input vector V (=V.sub.1, V.sub.2, . . . , V.sub.j, . . . , V.sub.n) corresponding to a 1 or 0 STATE of each LED array element, onto the optical mask 3a (or 3b).
The optical mask 3a (or 3b) has n by n elements constituting a matrix T (=T.sub.ij), each element having a different transmission factor. The output from the PD array 4a (or 4b) represents an output vector U (=U.sub.1, U.sub.2, . . . , U.sub.i, . . . , U.sub.n) of the data that passed through the matrix T. The j-th LED of the LED array 2a (or 2b) illuminates the J-th row element of the optical mask 3a (or 3b). The light that passed through the i-th column element of the optical mask 3a (or 3b) is received by the i-th photodiode of the PD array 4a (or 4b). These processes make up the vector matrix computation expressed by the following equation: ##EQU1##
The so-called neuro-computer accumulates data in the connecting strengths between neurons, The above-mentioned prior art computer, which is an optical association type neuro-computer accumulates a plurality of pieces of past data in the transmission factor T of each element of the optical mask. The data is accumulated by the rule expressed by the following equation (2) based on the Hopfield model: ##EQU2## where, character "s" is the number of pieces of the accumulated data. In the neuro-computer, the value T.sub.ij may be positive or negative. However, since negative values cannot occur in optical terms, the optical system is divided into two portions, one addressing the positive component value T.sub.ij.sup.(+) of the matrix T.sub.ij and the other dealing with the negative component value T.sub.ij.sup.(-) thereof. (Hereafter, the positive value T.sub.ij.sup.(+) is the transmission factor of the optical mask 3a, and the negative value T.sub.ij.sup.(-) is that of the optical mask 3b.) In this manner, output vectors U.sub.i.sup.(+) (of PD array 4a) and U.sub.i.sup.(-) (of PD array 4b) are generated as the result of the matrix computation given by Eq. (1), and the difference between the vectors EQU U.sub.i =U.sub.i.sup.(+) -U.sub.i.sup.(-) ( 3)
is obtained using the differential amplifier 5.
The output signal from the differential amplifier 5 is processed by the comparator 6 with respect to a threshold value, as expressed by the equation: EQU V.sub.i '=.theta.(U.sub.i) (4)
where, ##EQU3## Thereafter, the new input vector V.sub.i ' is fed back to the LED arrays 2a and 2b and is output to the data output section 7.
Illustratively, in the setup above, three pieces of data, say alphabetic characters A, J and E, may be accumulated beforehand in the optical masks 3a and 3b. When an incomplete piece of data, say A', is input to the LED arrays 2a and 2b, the internal feedback process is repeated and eventually yields data A, the closest to input data A', which is output for display as complete output data.
In other words, the energy of the system takes a minimum value with respect to accumulated data A, J and E, shown in FIG. 1. When the incomplete data is given, the whole system changes so that the nearest minimum value of its energy (i.e., the accumulated data closest to the incomplete data) is taken therein (by changing the illuminating status of the LED arrays 2a and 2b).
Because the prior art data processing apparatus is typically constructed as outlined above, electronic circuits such as the comparator need to be attached externally. This means that even single function equipment tends to be big in scale; multifunction apparatus that can deal flexibly with various problems are difficult to construct because of their huge physical dimensions.